The foundation of our semantic SLAM approach is our Surfel-based Mapping (SuMa) [1]} pipeline, which we extend by integrating semantic information provided by a semantic segmentation using the FCN RangeNet++ [2]} as illustrated in Fig. REF .
The point-wise labels are provided by RangeNet++ using spherical projections of the point clouds. This information is then used to filter dynamic objects and to add semantic constraints to the scan registration, which improves the robustness and accuracy of the pose estimation by SuMa.
